Grasp planning via hand-object geometric fitting

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Grasp planning is crucial for many robotic applications such as object manipulation and object transport. Planning stable grasps is a challenging problem. Many parameters such as object geometry, hand geometry and kinematics, hand-object contacts have to be considered, making the space of grasps too large to be exhaustively searched. This paper presents a general approach for planning grasps on 3D objects based on hand-object geometric fitting. Our key idea is to build a contact score map on a 3D object’s voxelization, and apply this score map and a hand’s kinematic parameters to find a set of target contacts on the object surface. Guided by these target contacts, we find grasps with a high quality measure by iteratively adjusting the hand pose and joint angles to fit the hand’s instantaneous geometric shape with the object’s fixed shape, during which the fitting process is speeded up by taking advantage of the discrete volumetric space. We demonstrate the effectiveness of our grasp planning approach on 3D objects of various shapes, poses, and sizes, as well as hand models with different kinematics. A comparison with two state-of-the-art approaches shows that our approach can generate grasps that are more likely to be stable, especially for objects with complex shapes.